In this paper, we propose a ground-based monocular UAV localisation system that detects and localises an LED marker attached to the underside of a UAV. Our system removes the need for extensive infrastructure and calibration unlike existing technologies such as UWB, radio frequency and multi-camera systems often used for localisation in GPS-denied environment. To improve deployablity for real-world applications without the need to collect extensive real dataset, we train a CNN on synthetic binary images as opposed to using real images in existing monocular UAV localisation methods, and factor in the camera's zoom to allow tracking of UAVs flying at further distances. We propose NoisyCutout algorithm for augmenting synthetic binary images to simulate binary images processed from real images and show that it improves localisation accuracy as compared to using existing salt-and-pepper and Cutout augmentation methods. We also leverage uncertainty propagation to modify the CNN's loss function and show that this also improves localisation accuracy. Real-world experiments are conducted to evaluate our methods and we achieve an overall 3D RMSE of approximately 0.41m.

翻译：本文提出了一种基于地面的单目无人机定位系统，该系统能够检测并定位安装在无人机底部的LED标志。与常用于GPS受限环境中的超宽带、射频和多相机系统等现有技术不同，我们的系统无需大量基础设施和标定。为提升实际应用中的可部署性，避免收集大量真实数据集，我们采用合成二值图像训练CNN，而非现有单目无人机定位方法中使用的真实图像，并考虑相机变焦因素以追踪更远距离飞行的无人机。我们提出NoisyCutout算法用于增强合成二值图像以模拟从真实图像处理得到的二值图像，实验表明，相比现有椒盐噪声和Cutout增强方法，该算法能提升定位精度。我们还利用不确定性传播来修改CNN的损失函数，结果表明这也提高了定位精度。通过实际实验评估我们的方法，最终实现了约0.41米的整体三维均方根误差。